Overview

Dataset statistics

Number of variables37
Number of observations923
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory266.9 KiB
Average record size in memory296.1 B

Variable types

Numeric9
Categorical28

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
age_first_funding_year is highly overall correlated with age_last_funding_yearHigh correlation
age_first_milestone_year is highly overall correlated with age_last_milestone_yearHigh correlation
age_last_funding_year is highly overall correlated with age_first_funding_year and 2 other fieldsHigh correlation
age_last_milestone_year is highly overall correlated with age_first_milestone_year and 1 other fieldsHigh correlation
age_startup_year is highly overall correlated with age_last_funding_year and 1 other fieldsHigh correlation
funding_rounds is highly overall correlated with funding_total_usdHigh correlation
funding_total_usd is highly overall correlated with age_last_funding_year and 6 other fieldsHigh correlation
has_Investor is highly overall correlated with has_Seed and 2 other fieldsHigh correlation
has_RoundABCD is highly overall correlated with funding_total_usd and 2 other fieldsHigh correlation
has_Seed is highly overall correlated with funding_total_usd and 3 other fieldsHigh correlation
has_VC is highly overall correlated with has_InvestorHigh correlation
has_angel is highly overall correlated with funding_total_usd and 1 other fieldsHigh correlation
has_roundA is highly overall correlated with has_RoundABCD and 1 other fieldsHigh correlation
has_roundB is highly overall correlated with funding_total_usdHigh correlation
has_roundC is highly overall correlated with funding_total_usdHigh correlation
is_CA is highly overall correlated with is_otherstateHigh correlation
is_otherstate is highly overall correlated with is_CAHigh correlation
milestones is highly overall correlated with age_last_milestone_yearHigh correlation
status is highly overall correlated with age_startup_yearHigh correlation
is_MA is highly imbalanced (56.4%)Imbalance
is_TX is highly imbalanced (73.3%)Imbalance
is_mobile is highly imbalanced (57.8%)Imbalance
is_enterprise is highly imbalanced (60.1%)Imbalance
is_advertising is highly imbalanced (64.5%)Imbalance
is_gamesvideo is highly imbalanced (68.7%)Imbalance
is_ecommerce is highly imbalanced (82.0%)Imbalance
is_biotech is highly imbalanced (77.2%)Imbalance
is_consulting is highly imbalanced (96.8%)Imbalance
has_roundD is highly imbalanced (53.2%)Imbalance
invalid_startup is highly imbalanced (80.9%)Imbalance
age_first_funding_year has 56 (6.1%) zerosZeros
age_last_funding_year has 17 (1.8%) zerosZeros
age_first_milestone_year has 197 (21.3%) zerosZeros
age_last_milestone_year has 171 (18.5%) zerosZeros
milestones has 152 (16.5%) zerosZeros

Reproduction

Analysis started2024-06-04 20:49:49.900795
Analysis finished2024-06-04 20:51:11.494260
Duration1 minute and 21.59 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

age_first_funding_year
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct607
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97673273
Minimum0
Maximum3.1309579
Zeros56
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:11.694088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.46570394
median0.90470386
Q31.5224736
95-th percentile2.0281067
Maximum3.1309579
Range3.1309579
Interquartile range (IQR)1.0567697

Descriptive statistics

Standard deviation0.64830481
Coefficient of variation (CV)0.66374842
Kurtosis-0.68433279
Mean0.97673273
Median Absolute Deviation (MAD)0.5032786
Skewness0.30607379
Sum901.52431
Variance0.42029913
MonotonicityNot monotonic
2024-06-04T20:51:11.999959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
6.1%
0.6931471806 13
 
1.4%
0.5584150674 11
 
1.2%
0.1545220394 9
 
1.0%
0.457931048 9
 
1.0%
0.3500933326 8
 
0.9%
0.08148781685 8
 
0.9%
1.099511884 7
 
0.8%
0.5103054885 6
 
0.7%
0.605899412 6
 
0.7%
Other values (597) 790
85.6%
ValueCountFrequency (%)
0 56
6.1%
0.002696361548 3
 
0.3%
0.008166562666 2
 
0.2%
0.01093994004 1
 
0.1%
0.01626697246 2
 
0.2%
0.01901800584 3
 
0.3%
0.02166363964 2
 
0.2%
0.02965588491 1
 
0.1%
0.03237038003 1
 
0.1%
0.0349809689 1
 
0.1%
ValueCountFrequency (%)
3.130957855 1
0.1%
3.082162232 1
0.1%
2.889610357 1
0.1%
2.857573046 1
0.1%
2.520346185 1
0.1%
2.512375892 1
0.1%
2.49693403 1
0.1%
2.477109665 1
0.1%
2.474814226 1
0.1%
2.464610401 1
0.1%

age_last_funding_year
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct750
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4224289
Minimum0
Maximum3.1309579
Zeros17
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:12.344436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28489148
Q11.0068362
median1.5140717
Q31.8812345
95-th percentile2.3381067
Maximum3.1309579
Range3.1309579
Interquartile range (IQR)0.87439832

Descriptive statistics

Standard deviation0.62541461
Coefficient of variation (CV)0.43968076
Kurtosis-0.4667102
Mean1.4224289
Median Absolute Deviation (MAD)0.42477064
Skewness-0.34458229
Sum1312.9019
Variance0.39114344
MonotonicityNot monotonic
2024-06-04T20:51:12.814540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
1.8%
0.457931048 4
 
0.4%
1.151963464 3
 
0.3%
1.21973788 3
 
0.3%
0.9502282829 3
 
0.3%
0.7834448185 3
 
0.3%
1.65286132 3
 
0.3%
1.792209368 3
 
0.3%
1.294535368 3
 
0.3%
0.915730575 3
 
0.3%
Other values (740) 878
95.1%
ValueCountFrequency (%)
0 17
1.8%
0.002696361548 2
 
0.2%
0.008166562666 1
 
0.1%
0.01626697246 2
 
0.2%
0.02965588491 1
 
0.1%
0.0533511755 1
 
0.1%
0.07649797097 1
 
0.1%
0.07899600623 1
 
0.1%
0.08148781685 3
 
0.3%
0.108944087 1
 
0.1%
ValueCountFrequency (%)
3.130957855 1
0.1%
3.082162232 1
0.1%
2.889610357 1
0.1%
2.857573046 1
0.1%
2.770874754 1
0.1%
2.710241133 1
0.1%
2.645302076 1
0.1%
2.595665071 1
0.1%
2.592796464 1
0.1%
2.55754509 1
0.1%

age_first_milestone_year
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct450
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0283971
Minimum0
Maximum3.2459033
Zeros197
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:13.369084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.26236368
median1.0995119
Q31.6099778
95-th percentile2.1978355
Maximum3.2459033
Range3.2459033
Interquartile range (IQR)1.3476141

Descriptive statistics

Standard deviation0.76225337
Coefficient of variation (CV)0.74120529
Kurtosis-1.1467798
Mean1.0283971
Median Absolute Deviation (MAD)0.64158084
Skewness0.034517561
Sum949.21052
Variance0.5810302
MonotonicityNot monotonic
2024-06-04T20:51:14.320533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 197
 
21.3%
1.609977767 26
 
2.8%
0.6931471806 17
 
1.8%
1.098612289 16
 
1.7%
1.386969133 15
 
1.6%
1.792209368 14
 
1.5%
1.099511884 13
 
1.4%
1.386294361 13
 
1.4%
2.080128805 11
 
1.2%
1.946295789 10
 
1.1%
Other values (440) 591
64.0%
ValueCountFrequency (%)
0 197
21.3%
0.02166363964 1
 
0.1%
0.02439988682 1
 
0.1%
0.02965588491 1
 
0.1%
0.0349809689 1
 
0.1%
0.03768106697 1
 
0.1%
0.0402778465 1
 
0.1%
0.04554681496 1
 
0.1%
0.06109509936 1
 
0.1%
0.06372569088 1
 
0.1%
ValueCountFrequency (%)
3.245903271 1
0.1%
2.871472056 1
0.1%
2.803275529 1
0.1%
2.719313201 1
0.1%
2.639842735 1
0.1%
2.602993343 1
0.1%
2.552650964 1
0.1%
2.532823417 1
0.1%
2.487644565 1
0.1%
2.483539048 1
0.1%

age_last_milestone_year
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct580
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3333924
Minimum0
Maximum3.2459033
Zeros171
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:14.788134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.69382173
median1.571466
Q31.9542877
95-th percentile2.3985159
Maximum3.2459033
Range3.2459033
Interquartile range (IQR)1.260466

Descriptive statistics

Standard deviation0.81786821
Coefficient of variation (CV)0.61337398
Kurtosis-1.0052886
Mean1.3333924
Median Absolute Deviation (MAD)0.50866279
Skewness-0.48174122
Sum1230.7212
Variance0.66890841
MonotonicityNot monotonic
2024-06-04T20:51:15.260838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 171
 
18.5%
1.609977767 21
 
2.3%
1.792209368 13
 
1.4%
1.946295789 11
 
1.2%
2.080128805 11
 
1.2%
0.6931471806 10
 
1.1%
1.099511884 9
 
1.0%
1.098612289 8
 
0.9%
1.386294361 8
 
0.9%
1.386969133 6
 
0.7%
Other values (570) 655
71.0%
ValueCountFrequency (%)
0 171
18.5%
0.0349809689 1
 
0.1%
0.03768106697 1
 
0.1%
0.04554681496 1
 
0.1%
0.07899600623 1
 
0.1%
0.08148781685 1
 
0.1%
0.09157594349 1
 
0.1%
0.10399959 1
 
0.1%
0.1259275566 1
 
0.1%
0.1283932148 1
 
0.1%
ValueCountFrequency (%)
3.245903271 1
0.1%
2.961311622 1
0.1%
2.803275529 1
0.1%
2.710241133 1
0.1%
2.680082732 1
0.1%
2.67801702 1
0.1%
2.639842735 1
0.1%
2.631399512 1
0.1%
2.624864225 1
0.1%
2.619095118 1
0.1%

funding_rounds
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3109426
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:15.770417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3909217
Coefficient of variation (CV)0.60188502
Kurtosis2.2645059
Mean2.3109426
Median Absolute Deviation (MAD)1
Skewness1.3569171
Sum2133
Variance1.9346632
MonotonicityNot monotonic
2024-06-04T20:51:16.134872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 317
34.3%
2 280
30.3%
3 167
18.1%
4 90
 
9.8%
5 40
 
4.3%
7 13
 
1.4%
6 13
 
1.4%
8 2
 
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
1 317
34.3%
2 280
30.3%
3 167
18.1%
4 90
 
9.8%
5 40
 
4.3%
6 13
 
1.4%
7 13
 
1.4%
8 2
 
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
10 1
 
0.1%
8 2
 
0.2%
7 13
 
1.4%
6 13
 
1.4%
5 40
 
4.3%
4 90
 
9.8%
3 167
18.1%
2 280
30.3%
1 317
34.3%

funding_total_usd
Real number (ℝ)

HIGH CORRELATION 

Distinct505
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.745752
Minimum9.3057415
Maximum22.463732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:16.657923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.3057415
5-th percentile12.135913
Q114.817937
median16.118096
Q317.023325
95-th percentile18.057409
Maximum22.463732
Range13.157991
Interquartile range (IQR)2.2053877

Descriptive statistics

Standard deviation1.8147998
Coefficient of variation (CV)0.11525647
Kurtosis1.2992274
Mean15.745752
Median Absolute Deviation (MAD)1.0296194
Skewness-0.96949318
Sum14533.329
Variance3.2934983
MonotonicityNot monotonic
2024-06-04T20:51:17.258736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.11809575 24
 
2.6%
15.42494867 21
 
2.3%
15.20180517 20
 
2.2%
13.81551156 16
 
1.7%
14.50865824 16
 
1.7%
16.52356083 14
 
1.5%
13.12236538 12
 
1.3%
14.91412318 11
 
1.2%
16.30041729 11
 
1.2%
15.06827381 9
 
1.0%
Other values (495) 769
83.3%
ValueCountFrequency (%)
9.305741457 1
 
0.1%
9.392745259 1
 
0.1%
9.615872145 2
 
0.2%
9.852246888 1
 
0.1%
9.903537551 7
0.8%
9.961284651 1
 
0.1%
10.1266711 2
 
0.2%
10.30898599 2
 
0.2%
10.34177474 1
 
0.1%
10.81979828 4
0.4%
ValueCountFrequency (%)
22.46373201 1
0.1%
20.04992129 1
0.1%
19.51762498 1
0.1%
19.2886632 1
0.1%
19.26224836 1
0.1%
19.16927683 1
0.1%
18.90473598 1
0.1%
18.81272284 1
0.1%
18.77133762 1
0.1%
18.72688797 1
0.1%

milestones
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8418202
Minimum0
Maximum8
Zeros152
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:17.776547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.322632
Coefficient of variation (CV)0.71811139
Kurtosis0.26032644
Mean1.8418202
Median Absolute Deviation (MAD)1
Skewness0.57737806
Sum1700
Variance1.7493555
MonotonicityNot monotonic
2024-06-04T20:51:18.188192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 250
27.1%
2 246
26.7%
3 182
19.7%
0 152
16.5%
4 62
 
6.7%
5 24
 
2.6%
6 6
 
0.7%
8 1
 
0.1%
ValueCountFrequency (%)
0 152
16.5%
1 250
27.1%
2 246
26.7%
3 182
19.7%
4 62
 
6.7%
5 24
 
2.6%
6 6
 
0.7%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 6
 
0.7%
5 24
 
2.6%
4 62
 
6.7%
3 182
19.7%
2 246
26.7%
1 250
27.1%
0 152
16.5%

is_CA
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
1
487 
0
436 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 487
52.8%
0 436
47.2%

Length

2024-06-04T20:51:18.464271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:18.723318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 487
52.8%
0 436
47.2%

Most occurring characters

ValueCountFrequency (%)
1 487
52.8%
0 436
47.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 487
52.8%
0 436
47.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 487
52.8%
0 436
47.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 487
52.8%
0 436
47.2%

is_NY
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
817 
1
106 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 817
88.5%
1 106
 
11.5%

Length

2024-06-04T20:51:18.933026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:19.233087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 817
88.5%
1 106
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 817
88.5%
1 106
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 817
88.5%
1 106
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 817
88.5%
1 106
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 817
88.5%
1 106
 
11.5%

is_MA
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
840 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 840
91.0%
1 83
 
9.0%

Length

2024-06-04T20:51:19.451629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:19.715130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 840
91.0%
1 83
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 840
91.0%
1 83
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 840
91.0%
1 83
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 840
91.0%
1 83
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 840
91.0%
1 83
 
9.0%

is_TX
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
881 
1
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 881
95.4%
1 42
 
4.6%

Length

2024-06-04T20:51:19.928705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:20.198035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 881
95.4%
1 42
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 881
95.4%
1 42
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 881
95.4%
1 42
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 881
95.4%
1 42
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 881
95.4%
1 42
 
4.6%

is_otherstate
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
719 
1
204 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 719
77.9%
1 204
 
22.1%

Length

2024-06-04T20:51:20.396437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:20.645118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 719
77.9%
1 204
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 719
77.9%
1 204
 
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 719
77.9%
1 204
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 719
77.9%
1 204
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 719
77.9%
1 204
 
22.1%

is_software
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
770 
1
153 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 770
83.4%
1 153
 
16.6%

Length

2024-06-04T20:51:20.850966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:21.102487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 770
83.4%
1 153
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 770
83.4%
1 153
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 770
83.4%
1 153
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 770
83.4%
1 153
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 770
83.4%
1 153
 
16.6%

is_web
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
779 
1
144 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 779
84.4%
1 144
 
15.6%

Length

2024-06-04T20:51:21.330210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:21.570700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 779
84.4%
1 144
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 779
84.4%
1 144
 
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 779
84.4%
1 144
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 779
84.4%
1 144
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 779
84.4%
1 144
 
15.6%

is_mobile
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
844 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 844
91.4%
1 79
 
8.6%

Length

2024-06-04T20:51:21.773262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:22.011213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 844
91.4%
1 79
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 844
91.4%
1 79
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 844
91.4%
1 79
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 844
91.4%
1 79
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 844
91.4%
1 79
 
8.6%

is_enterprise
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
850 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 850
92.1%
1 73
 
7.9%

Length

2024-06-04T20:51:22.226511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:22.486941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 850
92.1%
1 73
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 850
92.1%
1 73
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 850
92.1%
1 73
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 850
92.1%
1 73
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 850
92.1%
1 73
 
7.9%

is_advertising
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
861 
1
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 861
93.3%
1 62
 
6.7%

Length

2024-06-04T20:51:22.698075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:22.956873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 861
93.3%
1 62
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 861
93.3%
1 62
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 861
93.3%
1 62
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 861
93.3%
1 62
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 861
93.3%
1 62
 
6.7%

is_gamesvideo
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
871 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 871
94.4%
1 52
 
5.6%

Length

2024-06-04T20:51:23.157325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:23.406883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 871
94.4%
1 52
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 871
94.4%
1 52
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 871
94.4%
1 52
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 871
94.4%
1 52
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 871
94.4%
1 52
 
5.6%

is_ecommerce
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
898 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 898
97.3%
1 25
 
2.7%

Length

2024-06-04T20:51:23.605854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:23.834827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 898
97.3%
1 25
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 898
97.3%
1 25
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 898
97.3%
1 25
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 898
97.3%
1 25
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 898
97.3%
1 25
 
2.7%

is_biotech
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
889 
1
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 889
96.3%
1 34
 
3.7%

Length

2024-06-04T20:51:24.032993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:24.293066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 889
96.3%
1 34
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 889
96.3%
1 34
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 889
96.3%
1 34
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 889
96.3%
1 34
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 889
96.3%
1 34
 
3.7%

is_consulting
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
920 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 920
99.7%
1 3
 
0.3%

Length

2024-06-04T20:51:24.518707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:24.758781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 920
99.7%
1 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 920
99.7%
1 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 920
99.7%
1 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 920
99.7%
1 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 920
99.7%
1 3
 
0.3%

is_othercategory
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
625 
1
298 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 625
67.7%
1 298
32.3%

Length

2024-06-04T20:51:24.960108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:25.205861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 625
67.7%
1 298
32.3%

Most occurring characters

ValueCountFrequency (%)
0 625
67.7%
1 298
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 625
67.7%
1 298
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 625
67.7%
1 298
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 625
67.7%
1 298
32.3%

has_VC
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
622 
1
301 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 622
67.4%
1 301
32.6%

Length

2024-06-04T20:51:25.426161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:25.670552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 622
67.4%
1 301
32.6%

Most occurring characters

ValueCountFrequency (%)
0 622
67.4%
1 301
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 622
67.4%
1 301
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 622
67.4%
1 301
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 622
67.4%
1 301
32.6%

has_angel
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
688 
1
235 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 688
74.5%
1 235
 
25.5%

Length

2024-06-04T20:51:25.872957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:26.131427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 688
74.5%
1 235
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0 688
74.5%
1 235
 
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 688
74.5%
1 235
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 688
74.5%
1 235
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 688
74.5%
1 235
 
25.5%

has_roundA
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
1
469 
0
454 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 469
50.8%
0 454
49.2%

Length

2024-06-04T20:51:26.365309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:26.629640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 469
50.8%
0 454
49.2%

Most occurring characters

ValueCountFrequency (%)
1 469
50.8%
0 454
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 469
50.8%
0 454
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 469
50.8%
0 454
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 469
50.8%
0 454
49.2%

has_roundB
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
561 
1
362 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 561
60.8%
1 362
39.2%

Length

2024-06-04T20:51:26.833332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:27.096071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 561
60.8%
1 362
39.2%

Most occurring characters

ValueCountFrequency (%)
0 561
60.8%
1 362
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 561
60.8%
1 362
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 561
60.8%
1 362
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 561
60.8%
1 362
39.2%

has_roundC
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
708 
1
215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Length

2024-06-04T20:51:27.319449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:27.589236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

has_roundD
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
831 
1
92 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 831
90.0%
1 92
 
10.0%

Length

2024-06-04T20:51:27.792006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:28.060413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 831
90.0%
1 92
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 831
90.0%
1 92
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 831
90.0%
1 92
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 831
90.0%
1 92
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 831
90.0%
1 92
 
10.0%

avg_participants
Real number (ℝ)

Distinct59
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8385861
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:28.410143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median2.5
Q33.8
95-th percentile6
Maximum16
Range15
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.8746009
Coefficient of variation (CV)0.66039953
Kurtosis5.0697277
Mean2.8385861
Median Absolute Deviation (MAD)1.1667
Skewness1.7675537
Sum2620.015
Variance3.5141287
MonotonicityNot monotonic
2024-06-04T20:51:28.943285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 216
23.4%
2 152
16.5%
3 105
11.4%
4 71
 
7.7%
2.5 50
 
5.4%
5 38
 
4.1%
1.5 37
 
4.0%
3.5 27
 
2.9%
2.6667 21
 
2.3%
6 21
 
2.3%
Other values (49) 185
20.0%
ValueCountFrequency (%)
1 216
23.4%
1.25 1
 
0.1%
1.3333 6
 
0.7%
1.4 1
 
0.1%
1.5 37
 
4.0%
1.6667 16
 
1.7%
1.75 5
 
0.5%
2 152
16.5%
2.25 9
 
1.0%
2.3333 14
 
1.5%
ValueCountFrequency (%)
16 1
 
0.1%
12.5 1
 
0.1%
11.5 1
 
0.1%
11 1
 
0.1%
10 2
 
0.2%
9.5 2
 
0.2%
9.3333 1
 
0.1%
9 7
0.8%
8.6667 1
 
0.1%
8.5 3
0.3%

is_top500
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
1
747 
0
176 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 747
80.9%
0 176
 
19.1%

Length

2024-06-04T20:51:29.403076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:29.880642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 747
80.9%
0 176
 
19.1%

Most occurring characters

ValueCountFrequency (%)
1 747
80.9%
0 176
 
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 747
80.9%
0 176
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 747
80.9%
0 176
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 747
80.9%
0 176
 
19.1%

status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
1
597 
0
326 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 597
64.7%
0 326
35.3%

Length

2024-06-04T20:51:30.249030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:30.697469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 597
64.7%
0 326
35.3%

Most occurring characters

ValueCountFrequency (%)
1 597
64.7%
0 326
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 597
64.7%
0 326
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 597
64.7%
0 326
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 597
64.7%
0 326
35.3%

has_RoundABCD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
1
681 
0
242 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 681
73.8%
0 242
 
26.2%

Length

2024-06-04T20:51:31.606321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:32.084763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 681
73.8%
0 242
 
26.2%

Most occurring characters

ValueCountFrequency (%)
1 681
73.8%
0 242
 
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 681
73.8%
0 242
 
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 681
73.8%
0 242
 
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 681
73.8%
0 242
 
26.2%

has_Investor
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
1
498 
0
425 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 498
54.0%
0 425
46.0%

Length

2024-06-04T20:51:32.434311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:32.916974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 498
54.0%
0 425
46.0%

Most occurring characters

ValueCountFrequency (%)
1 498
54.0%
0 425
46.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 498
54.0%
0 425
46.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 498
54.0%
0 425
46.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 498
54.0%
0 425
46.0%

has_Seed
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
708 
1
215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Length

2024-06-04T20:51:33.368143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:33.670306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 708
76.7%
1 215
 
23.3%

invalid_startup
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
896 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 896
97.1%
1 27
 
2.9%

Length

2024-06-04T20:51:33.877399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:34.122226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 896
97.1%
1 27
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 896
97.1%
1 27
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 896
97.1%
1 27
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 896
97.1%
1 27
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 896
97.1%
1 27
 
2.9%

age_startup_year
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.128927
Minimum0
Maximum24
Zeros5
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-06-04T20:51:34.324807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113
median17
Q319
95-th percentile24
Maximum24
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.4140959
Coefficient of variation (CV)0.42396237
Kurtosis-0.30704725
Mean15.128927
Median Absolute Deviation (MAD)3
Skewness-0.84222179
Sum13964
Variance41.140627
MonotonicityNot monotonic
2024-06-04T20:51:35.235647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
17 172
18.6%
19 71
 
7.7%
18 63
 
6.8%
16 59
 
6.4%
15 56
 
6.1%
24 51
 
5.5%
20 46
 
5.0%
21 45
 
4.9%
22 45
 
4.9%
14 40
 
4.3%
Other values (15) 275
29.8%
ValueCountFrequency (%)
0 5
 
0.5%
1 21
2.3%
2 29
3.1%
3 31
3.4%
4 22
2.4%
5 32
3.5%
6 17
1.8%
7 19
2.1%
8 8
 
0.9%
9 9
 
1.0%
ValueCountFrequency (%)
24 51
 
5.5%
23 32
 
3.5%
22 45
 
4.9%
21 45
 
4.9%
20 46
 
5.0%
19 71
7.7%
18 63
 
6.8%
17 172
18.6%
16 59
 
6.4%
15 56
 
6.1%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
4
467 
3
241 
2
127 
1
88 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters923
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 467
50.6%
3 241
26.1%
2 127
 
13.8%
1 88
 
9.5%

Length

2024-06-04T20:51:35.746953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T20:51:36.031162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 467
50.6%
3 241
26.1%
2 127
 
13.8%
1 88
 
9.5%

Most occurring characters

ValueCountFrequency (%)
4 467
50.6%
3 241
26.1%
2 127
 
13.8%
1 88
 
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 467
50.6%
3 241
26.1%
2 127
 
13.8%
1 88
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 467
50.6%
3 241
26.1%
2 127
 
13.8%
1 88
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 467
50.6%
3 241
26.1%
2 127
 
13.8%
1 88
 
9.5%

Interactions

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2024-06-04T20:50:30.943921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:40.035573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:47.112300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-06-04T20:51:02.554185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:51:05.093048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:51:07.870032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:21.318199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:31.614783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:40.650792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:47.538926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:51.438442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:55.620584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:51:02.831425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:51:05.376220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:51:08.149257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:22.338644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:32.311839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:41.686480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-06-04T20:50:42.583286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-06-04T20:51:08.960857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-06-04T20:51:09.508412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:28.224583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:38.953688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:46.267126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:50.355218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:50:54.170016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:51:01.581064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:51:04.551140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T20:51:07.067026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-04T20:51:36.312794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
age_first_funding_yearage_first_milestone_yearage_last_funding_yearage_last_milestone_yearage_startup_yearavg_participantsfunding_roundsfunding_total_usdhas_Investorhas_RoundABCDhas_Seedhas_VChas_angelhas_roundAhas_roundBhas_roundChas_roundDinvalid_startupis_CAis_MAis_NYis_TXis_advertisingis_biotechis_consultingis_ecommerceis_enterpriseis_gamesvideois_mobileis_othercategoryis_otherstateis_softwareis_top500is_webmilestonesstatustier_relationships
age_first_funding_year1.0000.2230.6700.1390.3570.121-0.1780.2630.1730.1550.1490.1840.4320.3560.1570.0460.1420.0000.0360.0000.1440.0000.0000.2380.0000.1030.0200.0620.0000.1360.0970.1450.1320.159-0.3140.1930.093
age_first_milestone_year0.2231.0000.4240.8470.4220.1140.2550.3360.1030.2750.2560.0890.2260.2510.2860.2420.2810.0720.0500.0630.0000.0000.0000.0930.0460.0100.1100.1020.0000.0870.1160.1220.2210.0870.3770.4070.263
age_last_funding_year0.6700.4241.0000.3860.5160.2090.4220.6160.1660.3720.3500.3650.4020.2550.3640.3950.3420.1020.0690.0700.1070.0000.0000.1970.0000.1030.0000.0580.0000.1080.0000.1240.3390.226-0.1310.2710.073
age_last_milestone_year0.1390.8470.3861.0000.4630.1290.2980.3280.1220.3280.3100.0820.2380.2750.2950.2640.2440.0590.0730.0310.0590.0000.0000.0940.1430.0370.0800.1320.0750.0900.1500.0330.2910.0000.6200.4710.289
age_startup_year0.3570.4220.5160.4631.0000.2210.2000.4600.2280.3900.3680.1690.4090.3320.3220.3470.3030.1100.1050.0690.1360.0100.0000.0250.0000.1800.1310.0680.0000.0700.1430.1200.2840.1410.0610.7440.188
avg_participants0.1210.1140.2090.1290.2211.0000.1450.3410.0230.1490.1230.0000.1090.1200.1600.1890.2480.1820.0490.0000.1270.0000.0000.0140.0000.0250.0060.0000.0000.0570.0970.0000.2970.1550.0450.1360.066
funding_rounds-0.1780.2550.4220.2980.2000.1451.0000.5290.2510.3300.2800.3330.0790.3120.4880.4930.3540.1350.0950.0850.0590.0000.0000.0000.0630.0000.1170.0000.1040.0510.0710.0000.2750.0870.2440.2550.211
funding_total_usd0.2630.3360.6160.3280.4600.3410.5291.0000.3070.6460.6390.2090.5880.3550.5580.5380.4330.2550.1350.1490.1130.0570.0280.1770.0000.0660.0000.0000.0900.0880.1710.1760.4470.1990.0320.3010.209
has_Investor0.1730.1030.1660.1220.2280.0230.2510.3071.0000.4140.5060.6400.5370.2740.2240.1320.0660.1790.1000.0000.0460.0020.0000.0000.0000.0000.0000.0000.0320.0000.1130.0410.1030.011-0.0030.0830.097
has_RoundABCD0.1550.2750.3720.3280.3900.1490.3300.6460.4141.0000.9210.1250.4170.6030.4750.3240.1920.2820.1250.0730.0500.0420.0000.0000.0000.0670.0040.0000.0000.0000.1930.0470.3580.1020.1410.2510.274
has_Seed0.1490.2560.3500.3100.3680.1230.2800.6390.5060.9211.0000.1800.4740.5570.4390.2990.1760.0820.1290.0820.0440.0230.0000.0000.0000.0660.0000.0000.0000.0190.2010.0270.3410.100-0.1120.2310.262
has_VC0.1840.0890.3650.0820.1690.0000.3330.2090.6400.1250.1801.0000.2000.1940.0000.0460.0000.1090.0580.0000.0290.0000.0100.0000.0000.0000.0230.0440.0550.0000.0950.0250.0880.113-0.0980.0430.054
has_angel0.4320.2260.4020.2380.4090.1090.0790.5880.5370.4170.4740.2001.0000.1350.2870.2460.1200.0880.0470.0580.1410.0380.0170.0890.0000.0350.0000.0120.0210.0570.0050.1190.2170.1460.1470.0620.016
has_roundA0.3560.2510.2550.2750.3320.1200.3120.3550.2740.6030.5570.1940.1351.0000.2400.0000.0660.1670.0660.0000.0000.0000.0510.0580.0000.0000.0280.0200.0070.0960.1220.0000.1560.0520.3270.1790.336
has_roundB0.1570.2860.3640.2950.3220.1600.4880.5580.2240.4750.4390.0000.2870.2401.0000.3090.0780.1290.0850.0780.0540.0270.0000.0000.0000.0650.0460.0000.0000.0000.0930.0670.3010.0990.1700.2030.279
has_roundC0.0460.2420.3950.2640.3470.1890.4930.5380.1320.3240.2990.0460.2460.0000.3091.0000.3070.0820.0640.0550.0480.0060.0000.0360.0150.0160.0000.0000.0000.0510.1000.0000.2290.1230.0550.1600.255
has_roundD0.1420.2810.3420.2440.3030.2480.3540.4330.0660.1920.1760.0000.1200.0660.0780.3071.0000.0340.0720.0000.0470.0000.0420.0500.0000.0000.0000.0000.0000.0000.0390.0000.1440.0710.0030.1320.175
invalid_startup0.0000.0720.1020.0590.1100.1820.1350.2550.1790.2820.0820.1090.0880.1670.1290.0820.0341.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0400.0000.0090.0630.000-0.0860.0580.029
is_CA0.0360.0500.0690.0730.1050.0490.0950.1350.1000.1250.1290.0580.0470.0660.0850.0640.0720.0001.0000.3270.3760.2230.0000.0000.0000.0370.0000.0190.0000.0000.5600.0150.0900.0000.0390.0670.109
is_MA0.0000.0630.0700.0310.0690.0000.0850.1490.0000.0730.0820.0000.0580.0000.0780.0550.0000.0000.3271.0000.1020.0500.0000.0000.0000.0000.0000.0600.0000.0390.1600.0940.0730.0000.0120.0700.000
is_NY0.1440.0000.1070.0590.1360.1270.0590.1130.0460.0500.0440.0290.1410.0000.0540.0480.0470.0000.3760.1021.0000.0620.0500.0280.0000.0090.0150.1060.0000.0000.1850.0860.0000.0170.1100.0460.064
is_TX0.0000.0000.0000.0000.0100.0000.0000.0570.0020.0420.0230.0000.0380.0000.0270.0060.0000.0000.2230.0500.0621.0000.0350.0000.0040.0000.0260.0000.0000.0440.1050.0000.0000.000-0.0320.0220.000
is_advertising0.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0100.0170.0510.0000.0000.0420.0000.0000.0000.0500.0351.0000.0240.0000.0000.0630.0460.0670.1780.0000.1090.0240.1040.0720.0220.143
is_biotech0.2380.0930.1970.0940.0250.0140.0000.1770.0000.0000.0000.0000.0890.0580.0000.0360.0500.0000.0000.0000.0280.0000.0241.0000.0000.0000.0330.0130.0370.1250.0000.0720.0000.069-0.1950.0000.068
is_consulting0.0000.0460.0000.1430.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0040.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000-0.0090.0000.097
is_ecommerce0.1030.0100.1030.0370.1800.0250.0000.0660.0000.0670.0660.0000.0350.0000.0650.0160.0000.0000.0370.0000.0090.0000.0000.0000.0001.0000.0160.0000.0210.1030.0350.0570.0330.0530.0020.0560.000
is_enterprise0.0200.1100.0000.0800.1310.0060.1170.0000.0000.0040.0000.0230.0000.0280.0460.0000.0000.0210.0000.0000.0150.0260.0630.0330.0000.0161.0000.0540.0760.1950.0400.1210.0000.1160.0940.0610.087
is_gamesvideo0.0620.1020.0580.1320.0680.0000.0000.0000.0000.0000.0000.0440.0120.0200.0000.0000.0000.0000.0190.0600.1060.0000.0460.0130.0000.0000.0541.0000.0580.1600.0590.0970.0000.0930.0640.0000.000
is_mobile0.0000.0000.0000.0750.0000.0000.1040.0900.0320.0000.0000.0550.0210.0070.0000.0000.0000.0000.0000.0000.0000.0000.0670.0370.0000.0210.0760.0581.0000.2050.0000.1270.0120.1220.1340.0000.000
is_othercategory0.1360.0870.1080.0900.0700.0570.0510.0880.0000.0000.0190.0000.0570.0960.0000.0510.0000.0400.0000.0390.0000.0440.1780.1250.0000.1030.1950.1600.2051.0000.0000.3030.0440.292-0.1350.0230.110
is_otherstate0.0970.1160.0000.1500.1430.0970.0710.1710.1130.1930.2010.0950.0050.1220.0930.1000.0390.0000.5600.1600.1850.1050.0000.0000.0000.0350.0400.0590.0000.0001.0000.0230.1470.000-0.1280.1630.172
is_software0.1450.1220.1240.0330.1200.0000.0000.1760.0410.0470.0270.0250.1190.0000.0670.0000.0000.0090.0150.0940.0860.0000.1090.0720.0000.0570.1210.0970.1270.3030.0231.0000.0110.185-0.1580.0000.069
is_top5000.1320.2210.3390.2910.2840.2970.2750.4470.1030.3580.3410.0880.2170.1560.3010.2290.1440.0630.0900.0730.0000.0000.0240.0000.0000.0330.0000.0000.0120.0440.1470.0111.0000.1100.1770.3060.232
is_web0.1590.0870.2260.0000.1410.1550.0870.1990.0110.1020.1000.1130.1460.0520.0990.1230.0710.0000.0000.0000.0170.0000.1040.0690.0000.0530.1160.0930.1220.2920.0000.1850.1101.0000.1740.0000.000
milestones-0.3140.377-0.1310.6200.0610.0450.2440.032-0.0030.141-0.112-0.0980.1470.3270.1700.0550.003-0.0860.0390.0120.110-0.0320.072-0.195-0.0090.0020.0940.0640.134-0.135-0.128-0.1580.1770.1741.0000.3520.309
status0.1930.4070.2710.4710.7440.1360.2550.3010.0830.2510.2310.0430.0620.1790.2030.1600.1320.0580.0670.0700.0460.0220.0220.0000.0000.0560.0610.0000.0000.0230.1630.0000.3060.0000.3521.0000.380
tier_relationships0.0930.2630.0730.2890.1880.0660.2110.2090.0970.2740.2620.0540.0160.3360.2790.2550.1750.0290.1090.0000.0640.0000.1430.0680.0970.0000.0870.0000.0000.1100.1720.0690.2320.0000.3090.3801.000

Missing values

2024-06-04T20:51:10.020861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-04T20:51:11.067240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

age_first_funding_yearage_last_funding_yearage_first_milestone_yearage_last_milestone_yearfunding_roundsfunding_total_usdmilestonesis_CAis_NYis_MAis_TXis_otherstateis_softwareis_webis_mobileis_enterpriseis_advertisingis_gamesvideois_ecommerceis_biotechis_consultingis_othercategoryhas_VChas_angelhas_roundAhas_roundBhas_roundChas_roundDavg_participantsis_top500statushas_RoundABCDhas_Investorhas_Seedinvalid_startupage_startup_yeartier_relationships
01.1784401.3869691.7349252.041753312.83468431000000000000010100001.000001011017.04
11.8125422.3976502.0801292.080129417.50688711000000010000001001114.750011110024.03
20.7094630.7094630.8991451.164868114.77102221000001000000000010004.000011100015.04
31.4186411.8429441.9462961.946296317.50439011000010000000000001113.333311100022.04
40.0000000.9815170.0376810.037681214.07787611000000000100001100001.00001001101.04
51.7129331.7129331.7922091.792209115.83041411000000000000010001003.000010100017.04
61.0008161.8263221.3862942.029227317.07360721000010000000001011001.666711110019.03
70.9732762.0491881.8879032.123650317.34480831000000000000010011013.500011100020.01
81.5230742.4942212.1978362.397395316.08246940010000100000001010014.000011110022.02
90.9825281.7378141.3655301.962219315.56471141000001000000001110001.000011110019.02
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9141.4383411.4383411.3869691.726670114.80876320100000000100000010002.000011100017.04
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9221.4159751.4159751.6099781.609978116.81124311000000000000010001003.000011100021.04

Duplicate rows

Most frequently occurring

age_first_funding_yearage_last_funding_yearage_first_milestone_yearage_last_milestone_yearfunding_roundsfunding_total_usdmilestonesis_CAis_NYis_MAis_TXis_otherstateis_softwareis_webis_mobileis_enterpriseis_advertisingis_gamesvideois_ecommerceis_biotechis_consultingis_othercategoryhas_VChas_angelhas_roundAhas_roundBhas_roundChas_roundDavg_participantsis_top500statushas_RoundABCDhas_Investorhas_Seedinvalid_startupage_startup_yeartier_relationships# duplicates
00.5631521.6889331.3306761.609978417.34919721000000000000011011102.2511110016.022